Harnessing Large Language Models for Extracting Information from Unstructured Data

Large Language Models for Extracting Information

Harnessing Large Language Models for Extracting Information from Unstructured Data: The world we live in today is characterized by unstructured data and large language models that are used for extracting information. Unstructured data is prevalent across various domains, and we interact with it every day.  For instance, research papers, health documents, and financial statements are often presented in PDF format, filled with text, tables, and images. 

A routine task for data scientists over the past decade has been to extract crucial information from a huge chunk of unstructured data. The ability to shift through and get the crucial data from a large section of words and tables is important for generating actionable insights. Whether it’s extracting vital values from medical documents or tracking the number of times the word ‘artificial intelligence’ has been used by big tech companies in their presentations, developing effective extraction techniques becomes increasingly important for businesses to stay ahead in competition.  

Back in the day, data scientists used techniques like regular expressions (regex) and named entity recognition (NER) models to extract information. Regular expressions are patterns that are used to search for and manipulate text based on specific sequences .On the other hand NER models identify and classify entities such as names, dates, and locations within text. Although the methods NER and Regex have been useful, they often require precise configuration and can be limited in handling complex data . Now, with the advent of large language models (LLMs), we have a more advanced tool that can perform these extraction tasks with significantly higher accuracy.

Understanding LLMs: The Buzz Behind the Hype 

Large Language Models (LLMs) are a class of advanced artificial intelligence models designed to understand and generate human-like text. These models, such as OpenAI’s GPT-4, Meta’s llama-3.1 are trained on vast datasets containing text from books, articles, and websites. 

What truly sets them apart from anything else is their ability to understand the context around the data. For example, when we say the word “Bank,” we often refer to a financial institution that stores and lends money. However, when we mention “Bank Account,” we mean an account created in a bank under our name, storing our money. In contrast, when we talk about the “Bank of the river,” we’re referring to the land alongside the river. Even though all these phrases include the word “Bank,” they mean completely different things, and LLMs excel at understanding these nuances in context. 

While understanding the underlying architecture of LLMs—such as attention mechanisms and word embeddings—is beyond the scope of this article. But it’s clear that no current technology matches LLMs in their ability to understand textual data. This deep understanding is crucial for accurately extracting relevant information from the vast sea of words that unstructured data presents.  

Leveraging the power of LLM’s 

To demonstrate the power of LLMs, we’ll dive into a practical example: extracting vital values from medical texts. This task is crucial in healthcare, where accurate and timely information extraction can significantly impact patient outcomes. For this demonstration, we’ll be using the Llama 8B parameter model in combination with Lang Chain. 

Llama (Large Language Model Meta AI): Llama is a series of advanced language models developed by Meta (formerly Facebook). The “8B” in Llama 8B refers to the model’s 8 billion parameters, which are the components of the model that have been learned from vast amounts of text data. 

Lang Chain: Lang Chain is a framework designed to streamline the integration and orchestration of language models like Llama. It provides tools and abstractions that make it easier to work with language models, allowing developers to build applications that can interact with and leverage the capabilities of these models. 

For this demonstration, we will be using Jupiter notebook for the ease of understanding 

Here is an example of how we can define a template in Lang Chain to direct the model in the correct format: 

Once the template is established, we can use template to interact with the Llama model. Here’s how to set up and execute a model call: 

The sample input can be as follows, 

Output could be as follows

The example provided has been clearly oversimplified, The real-world data is far more complex than the example provided above, and models sometimes fail to produce structured outputs. To mitigate this issue, we can enhance prompt effectiveness by: 

1. Creating Detailed Prompts: Provide comprehensive instructions that guide the model towards structured responses. 
2. Including Examples: Supply examples of the desired output format to improve the model’s understanding. 
3. Allowing Processing Time: Give the model sufficient context and time to generate accurate outputs. 

Given below is an example of that kind of prompt, 

With such detailed prompts, we can significantly enhance the accuracy and structure of the model’s output.

Challenges with LLM models 

Despite the advantages the LLM models provide, they have a few drawbacks which holds them back in widespread use among the Data Scientist community 

1. Lack of structured output 

A good analogy for this would be, if we ask a person to recall a same story couple of times, The person will not  use the exact same words to describe the story. Rather he will say it in a way that the meaning will remain the same. These LLM models are designed to mimic human abilities. But, due to their probabilistic nature of their output, they do not produce the exact structure all the time.  

Currently, OpenAI, a frontrunner in AI technology, is working on an API for their ChatGPT model.This models aims to provide structured outputs more reliably. However, as of August 2024, this feature has yet to be released. 

2. Massive computational resources

Running LLM models requires an immense amount of computational power, which is both expensive and necessitates specialized knowledge. The high resource demand makes it difficult for the general public to run these models on standard hardware. As a result, only those with access to powerful computational resources and expertise can fully leverage these advanced models. 

3. Interpretability and Transparency

One of the ongoing challenges with LLM models is their lack of interpretability. These models function as “black boxes,” making it difficult to understand how they arrive at specific decisions or outputs. For data scientists and stakeholders who require transparency, especially in critical domains like healthcare or finance, this opacity can be a significant drawback. It’s not just about getting the right output; it’s also about understanding the “why” behind it. 

4. Environmental Impact

The environmental impact of training and deploying large LLMs is an often-overlooked challenge. The energy consumption required to train these models is enormous, contributing to a significant carbon footprint. As awareness of environmental issues grows, the sustainability of deploying such resource-intensive models is becoming an important consideration. 

Follow us on our LinkedIn Platform

Conclusion: Large Language Models for Extracting Information

In a world increasingly dominated by unstructured data, the rise of large language models (LLMs) represents a significant leap. This helpsin our ability to extract valuable insights from vast and complex datasets. From extracting vital information in medical texts to identifying key entities in legal documents, LLMs offer a powerful tool. This tool is transforming the way data scientists approach their work. However, the journey to fully harnessing the potential of these models is not without its challenges. Issues such as the lack of consistent structured output, and the environmental impact of their deployment all pose significant hurdles. 

Despite these challenges, the future of LLMs is bright. With ongoing advancements and a deeper understanding of how to optimize these models, we are on the cusp of a new era in data science.This where where the power of LLMs can be fully leveraged to turn unstructured data into actionable intelligence. This can happen all while navigating the complexities and ethical considerations that come with such powerful tools. As technology evolves, so will our ability to deploy it in ways that are both effective and responsible. This will ensure that LLMs become an indispensable part of the data scientist’s toolkit. 

If you need assistance with AI initiatives in your organization, please reach out to us for free consulting at info@medintelx.com